code: def test_init(self):
      # colCateg >> >> >> >> >> >> >> >> >> >> >> >> >> >> >> >> >> >> >> >> >> >> >> >> >> >>
      fbt = FeatureBinarizerFromTrees(colCateg=self.col_categorical)
      self.assertListEqual(fbt.colCateg, self.col_categorical)
  
      # treeNum  >> >> >> >> >> >> >> >> >> >> >> >> >> >> >> >> >> >> >> >> >> >> >> >> >> >>
      with self.assertRaises(ValueError):
          fbt = FeatureBinarizerFromTrees(treeNum=None)
  
      with self.assertRaises(ValueError):
          fbt = FeatureBinarizerFromTrees(treeNum=0)
  
      with self.assertRaises(ValueError):
          fbt = FeatureBinarizerFromTrees(treeNum=-1)
  
      fbt = FeatureBinarizerFromTrees(treeNum=3)
      self.assertEqual(fbt.treeNum, 3)
  
      # treeDepth >> >> >> >> >> >> >> >> >> >> >> >> >> >> >> >> >> >> >> >> >> >> >> >> >> >>
      with self.assertRaises(ValueError):
          fbt = FeatureBinarizerFromTrees(treeDepth=0)
  
      with self.assertRaises(ValueError):
          fbt = FeatureBinarizerFromTrees(treeDepth=-1)
  
      fbt = FeatureBinarizerFromTrees(treeDepth=3)
      self.assertEqual(fbt.treeDepth, 3)
      self.assertEqual(fbt.treeKwargs["max_depth"], 3)
  
      fbt = FeatureBinarizerFromTrees(
          treeDepth=None, treeKwargs=dict(max_depth=5)
      )
      self.assertEqual(fbt.treeKwargs["max_depth"], 5)
      self.assertEqual(fbt.treeDepth, 5)
  
      # treeFeatureSelection  >> >> >> >> >> >> >> >> >> >> >> >> >> >> >> >> >> >> >> >> >> >>
      with self.assertRaises(ValueError):
          fbt = FeatureBinarizerFromTrees(treeFeatureSelection=0)
  
      with self.assertRaises(ValueError):
          fbt = FeatureBinarizerFromTrees(treeFeatureSelection=-1)
  
      with self.assertRaises(ValueError):
          fbt = FeatureBinarizerFromTrees(treeFeatureSelection=3)
  
      with self.assertRaises(ValueError):
          fbt = FeatureBinarizerFromTrees(
              treeFeatureSelection="bad string value"
          )
  
      fbt = FeatureBinarizerFromTrees(treeFeatureSelection=0.4)
      self.assertEqual(fbt.treeFeatureSelection, 0.4)
      self.assertEqual(fbt.treeKwargs["max_features"], 0.4)
  
      fbt = FeatureBinarizerFromTrees(treeFeatureSelection=None)
      self.assertTrue(fbt.treeFeatureSelection is None)
      self.assertTrue(fbt.treeKwargs["max_features"] is None)
  
      fbt = FeatureBinarizerFromTrees(treeFeatureSelection="log2")
      self.assertEqual(fbt.treeFeatureSelection, "log2")
      self.assertEqual(fbt.treeKwargs["max_features"], "log2")
  
      fbt = FeatureBinarizerFromTrees(
          treeFeatureSelection=None, treeKwargs=dict(max_features=0.2)
      )
      self.assertEqual(fbt.treeKwargs["max_features"], 0.2)
      self.assertEqual(fbt.treeFeatureSelection, 0.2)
  
      # threshRound >> >> >> >> >> >> >> >> >> >> >> >> >> >> >> >> >> >> >> >> >> >> >> >> >>
      fbt = FeatureBinarizerFromTrees(threshRound=None)
  
      with self.assertRaises(ValueError):
          FeatureBinarizerFromTrees(threshRound=-1)
  
      fbt = FeatureBinarizerFromTrees(threshRound=3)
      self.assertTrue(fbt.threshRound == 3)
  
      # threshStr > >> >> >> >> >> >> >> >> >> >> >> >> >> >> >> >> >> >> >> >> >> >> >> >> >>
      fbt = FeatureBinarizerFromTrees(threshStr=True)
      self.assertTrue(fbt.threshStr)
      fbt = FeatureBinarizerFromTrees(threshStr=False)
      self.assertFalse(fbt.threshStr)
  
      # returnOrd   >> >> >> >> >> >> >> >> >> >> >> >> >> >> >> >> >> >> >> >> >> >> >> >> >>
      fbt = FeatureBinarizerFromTrees(returnOrd=True)
      self.assertTrue(fbt.returnOrd)
      fbt = FeatureBinarizerFromTrees(returnOrd=False)
      self.assertFalse(fbt.returnOrd)
  
      # randomState >> >> >> >> >> >> >> >> >> >> >> >> >> >> >> >> >> >> >> >> >> >> >> >> >> >>
      fbt = FeatureBinarizerFromTrees(randomState=3)
      self.assertEqual(fbt.randomState, 3)
  
file_path: AIX360/tests/rbm/test_Feature_Binarizer_From_Trees.py
imports: from unittest import TestCase
  import numpy
  from numpy import ndarray
  from pandas import DataFrame
  from sklearn.datasets import load_breast_cancer
  from sklearn.model_selection import train_test_split
  from sklearn.preprocessing import OneHotEncoder, StandardScaler
  from aix360.algorithms.rbm import FeatureBinarizerFromTrees
label_desc: Use image node_id 2 for calling the TestFeatureBinarizerFromTrees obj's underlying member method code with example usage: obj.test_init() without return types